arXiv subjects

Benjamin Udofia

Publications and source records attributed to Benjamin Udofia.

4 recordsLinked to original sources

arXiv2026

Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning

Understanding and inferring dislocation microstructures from diffraction patterns remains an open challenge in materials characterization, as diffraction measurements provide only indirect information about the underlying dislocation structure. In this work, a cross-modal learning framework is developed to enable the prediction of 3D dislocation structures directly from diffraction data. Dislocation density fields generated from discrete dislocation dynamics simulations are paired with corresponding virtual X-ray diffraction patterns and embedded into a shared 2D latent space using contrastive learning. The alignment between structural and diffraction representations of dislocation structures is evaluated directly in the learned latent space using correlations between corresponding latent features. To estimate the role of dataset size for this approach, farthest point sampling is employed to construct representative and diverse training subsets of varying sizes. The results show strong cross-modal alignment and that model performance improves rapidly with increasing dataset size. Near-saturation is achieved with approximately 500 representative observations from a dataset of 10,000 observations, enabling accurate prediction of dislocation density fields from previously unseen diffraction data of the same distribution. Qualitative comparisons confirm that the predicted structures capture the dominant spatial features of the underlying dislocation microstructures. These findings demonstrate an efficient approach for learning structure-diffraction relationships and highlight the potential for inferring structural characteristics of dislocation networks directly from diffraction patterns, providing a pathway toward diffraction-based structural analysis and future extension to experimental data.

cond-mat.mtrl-sci↗

arXiv2025

Control of ferroelectric domain wall dynamics by point defects: Insights from ab initio based simulations

The control of ferroelectric domain walls and their dynamics on the nanoscale becomes increasingly important for advanced nanoelectronics and novel computing schemes. One common approach to tackle this challenge is the pinning of walls by point defects. The fundamental understanding on how different defects influence the wall dynamics is, however, incomplete. In particular, the important class of defect dipoles in acceptor-doped ferroelectrics is currently underrepresented in theoretical work. In this study, we combine molecular dynamics simulations based on an \textit{ab\ initio}-derived effective Hamiltonian and methods from materials informatics, and analyze the impact of these defects on the motion of 180$^{\circ}$ domain walls in tetragonal BaTiO$_3$. We show how these defects can act as local pinning centers and restoring forces on the domain structure. Furthermore, we reveal how walls can flow around sparse defects by nucleation and growth of dipole clusters, and how pinning, roughening and bending of walls depend on the defect distribution. Surprisingly, the interaction between acceptor dopants and walls is short-ranged. We show that the limiting factor for the nucleation processes underlying wall motion is the defect-free area in front of the wall.

cond-mat.mtrl-sci↗

arXiv2024

Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints

Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for dislocation networks evolving as a consequence of plastic deformation of crystalline systems. Our study employs Isomap, a manifold learning technique, to unveil the intrinsic structure of high-dimensional density field data of dislocation structures from different compression axis. The resulting maps provide a systematic framework for quantitatively comparing dislocation structures, offering unique fingerprints based on density fields. Our novel, unbiased approach contributes to the quantitative classification of dislocation structures which can be systematically extended.

cond-mat.mtrl-sci↗

arXiv2023

Microscopic insights on field induced switching and domain wall motion in orthorhombic ferroelectrics

Surprisingly little is known about the microscopic processes that govern ferroelectric switching in orthorhombic ferroelectrics. To study microscopic switching processes we combine ab initio-based molecular dynamics simulations and data science on the prototypical material BaTiO$_3$. We reveal two different field regimes: For moderate field strengths, the switching is dominated by domain wall motion while a fast bulk-like switching can be induced for large fields. Switching in both field regimes follows a multi-step process via polarization directions perpendicular to the applied field. In the former case, the moving wall is of Bloch character and hosts dipole vortices due to nucleation, growth, and crossing of two dimensional 90$^{\circ}$ domains. In the second case, the local polarization shows a continuous correlated rotation via a an intermediate tetragonal multidomain state.

cond-mat.mtrl-sci↗